On the Rate of Convergence of the Bagged Nearest Neighbor Estimate

By Gérard Biau, Frédéric Cérou, Arnaud Guyader. 2010

83 citations recorded by the source corpus.

Subfields: Optimization & Training, Statistical Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Optimization & Training

1. Richard J. Samworth
2. Susan Athey
3. Stefan Wager
4. Rajen Dinesh Shah
5. Gérard Biau
6. Erwan Scornet

In Statistical Learning Theory

1. Stefan Wager
2. Susan Athey
3. Erwan Scornet
4. Richard J. Samworth
5. Rajen Dinesh Shah
6. Gérard Biau
7. Velibor V. Misic
8. Julie Tibshirani

Citations in the corpus

• Analysis of a Random Forests Model (2010, background): “This subspace randomization scheme is blended with bagging (Breiman, 1996; B ühlmann and Yu, 2002; Buja and Stuetzle, 2006; Biau et al., 2010) to resample, with replacement, the training da ta set each time a new individual tree is grown. Although the mechanism appears simple, it involves many diffe”
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, methodology): “In this case, as shown by Breiman [2001a], it is often better to generate many different decent-looking trees and average their predictions, instead of seeking a single highly-optimized tree.”
• A random forest guided tour (2015, background): “In Breiman’s (2001) forests, the final prediction is the average of the individual tree outcomes.”
• Variable selection with error control: another look at stability selection (2011, methodology): “Indeed, this is so in other situations where bagging and subagging have been successfully applied, such as classification trees (Breiman, 1996) or nearest neighbour classifiers (Hall and Samworth, 2005; Biau et al., 2010; Samworth, 2011). An alternative to subsampling complementary pairs would be to”